AI Workbench)

DXC Technology•Washington, DC

1d•Onsite

About The Position

The DevOps/DataOps Engineer at DXC Technology builds and operates the deployment and data foundations for AI Workbench. This role involves automating infrastructure provisioning, managing Kubernetes/container operations, building CI/CD pipelines, and implementing secure data pipelines and observability to ensure AI-driven solutions run reliably in private or hybrid environments. DXC Technology helps global companies run their mission-critical systems and operations, modernizing IT, optimizing data architectures, and ensuring security and scalability across various cloud environments.

Requirements

3+ years in DevOps/SRE and/or DataOps roles supporting enterprise platforms.
Hands-on experience with Kubernetes and container tooling.
Experience building CI/CD pipelines and IaC automation.
Strong knowledge of security practices in platform operations.
Experience with data pipelines and ETL/ELT concepts.
Secret Clearance and U.S. Citizenship.
5+ years of experience in database administration and management.

Nice To Haves

Experience supporting AI/ML platforms, GPU workloads, or model inference services.
Experience with policy-as-code (OPA/Gatekeeper-like concepts) and compliance-driven operations.
Familiarity with vector databases and search indexing operations.
Experience with hybrid connectivity and network security patterns.

Responsibilities

Provision and manage environments using Infrastructure as Code (IaC) for compute, networking, storage, and security controls.
Administer Kubernetes/container platforms to host AI Workbench services, agent runtimes, model endpoints, and supporting components.
Build and maintain CI/CD pipelines for applications/services, agent configurations, and infrastructure updates with automated checks.
Implement DataOps pipelines for RAG ingestion: secure connectors, preprocessing jobs, scheduling, data quality checks, and lineage tracking.
Implement observability: logs, metrics, traces, dashboards, alerting, and SLO/SLI monitoring across platform and workloads.
Harden environments: secrets management, vulnerability scanning, image signing, policy-as-code, and least-privilege access.
Support release management, incident response, and operational handover including runbooks and knowledge transfer.
Optimize performance and cost: resource sizing, autoscaling policies, GPU scheduling, and storage optimization.